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Deep convolutional neural networks (CNNs) have greatly improved the FaceRecognition (FR) performance in recent years. Almost all CNNs in FR are trainedon the carefully labeled datasets containing plenty of identities. However,such high-quality datasets are very expensive to collect, which restricts manyresearchers to achieve state-of-the-art performance. In this paper, we proposea framework, called SeqFace, for learning discriminative face features. Besidesa traditional identity training dataset, the designed SeqFace can train CNNs byusing an additional dataset which includes a large number of face sequencescollected from videos. Moreover, the label smoothing regularization (LSR) and anew proposed discriminative sequence agent (DSA) loss are employed to enhancediscrimination power of deep face features via making full use of the sequencedata. Our method achieves excellent performance on Labeled Faces in the Wild(LFW), YouTube Faces (YTF), only with a single ResNet. The code and models arepublicly available on-line (https://github.com/huangyangyu/SeqFace).

SeqFace: Make full use of sequence information for face recognition